Getting under—and through—the skin: ecological genomics of chytridiomycosis infection in frogs
Bibliographic record
Abstract
Amphibian species around the world are currently becoming endangered or lost at a rate that outstrips other vertebrates—victims of a combination of habitat loss, climate change and susceptibility to emerging infectious disease ( Stuart et al. 2004 ). One of the most devastating such diseases is caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), which infects hundreds of amphibian species on multiple continents. While Bd itself has been characterized for some time, we still know little about the mechanisms that make it so deadly. In this issue of Molecular Ecology, Rosenblum et al. describe a genomic approach to this question, reporting the results of a genome‐wide analysis of the transcriptional response to Bd in the liver, skin and spleen of mountain yellow‐legged frogs (Rana mucosa and R. sierrae: Fig. 1 ) ( Rosenblum et al. 2012 ). Their results indicate that the skin is not only the first, but likely the most important, line of defence in these animals. Strikingly, they describe a surprisingly modest immune response to infection in Rana, a result that may help explain variable Bd susceptibility across populations and species. The frog and the fungus. Left, the mountain yellow‐legged frog, one of hundreds of worldwide amphibian species in decline. Right, the chytrid fungus Batrachochytrium dendrobatidis (Bd), in part responsible for loss of these frogs. In Rana mucosa and R. sierrae, infection by Bd leads to a massive loss of skin integrity and frequently death. Photo credit: (left panel) Roland A. Knapp, Sierra Nevada Aquatic Research Lab; (right panel) Erica B. Rosenblum, University of Idaho. image
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".